Systems and methods for application aware slicing in 5G layer 2 and layer 1 using fine grain scheduling

ABSTRACT

Advances in wireless technologies have resulted in the ability of a 5G communication system to support multiple wireless communication applications. Each of these applications requires special handling in all layers and more so in scheduler and physical layer. The present disclosure presents embodiments of dynamical creating a computation instance with a slice of resources allocated for each scheduling input. Each computation instance may be independently managed, controlled, and customized according to the specific requirements of the corresponding scheduling input. Such a dynamic resource allocation allows large number of slices in PHY layer. Furthermore, when overloading happens, one scheduling inputs may be migrated from one distribution unit (DU) to another DU without interruption for end users during scheduling migration. Accordingly, efficiency and robustness of a 5G communication system may be improved to serve multiple wireless communication applications.

CROSS REFERENCE TO RELATED PATENT APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/366,677, entitled “SYSTEMS AND METHODS FOR APPLICATION AWARE SLICINGIN 5G LAYER 2 AND LAYER 1 USING FINE GRAIN SCHEDULING”, naming inventorsas Vinay Ravuri and Sriram Rajagopal, and filed on Jul. 2, 2021, whichapplication is hereby incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to a unified wirelessarchitecture that manages multiple wireless communication applicationsand data processing associated therewith. More particularly, the presentdisclosure relates to a processing architecture that manages multiplewireless communication applications in 5G layer 2 and layer 1 using finegrain scheduling.

BACKGROUND

The importance of wireless communication in today's society is wellunderstood by one of skill in the art. Advances in wireless technologieshave resulted in the ability of a 5G communication system to supportmultiple types of wireless communication applications, e.g., drones,Ultra-Reliable Low-Latency Communication (URLLC), Internet of things(IoT) devices, enhanced Mobile Broadband (eMBB), etc. Each type ofcommunication applications may have their own requirements orpreferences in layer 2 (scheduler and Medium Access Control (MAC)) andlayer 1 (physical layer or PHY).

Having a same design for all these applications may be inefficient foroverall system performance and operation. While on the other hand,different hardware solutions for at least PHY acceleration andseparate/different layer 2 schedulers may be used for different types ofapplications. However, such an implementation with multiple specificdesigns means multiple variants of products or solutions, and thusincreases cost and complexity. The deployment of such a solution is alsodifficult.

Accordingly, what is needed are systems, devices and methods thataddress the above-described issues.

BRIEF DESCRIPTION OF THE DRAWINGS

References will be made to embodiments of the disclosure, examples ofwhich may be illustrated in the accompanying figures. These figures areintended to be illustrative, not limiting. Although the accompanyingdisclosure is generally described in the context of these embodiments,it should be understood that it is not intended to limit the scope ofthe disclosure to these particular embodiments. Items in the figures maynot be to scale.

FIG. 1 depicts various open radio access network (RAN) deployments for atelecommunication service provider, according to embodiments of thepresent disclosure.

FIG. 2 depicts a central control unit for managing multiple wirelessscheduling inputs across various applications, according to embodimentsof the present disclosure.

FIG. 3 depicts a process of allocating resources among multiple wirelessscheduling inputs, according to embodiments of the present disclosure.

FIG. 4 depicts a schematic diagram for PHY policy implementation,according to embodiments of the present disclosure.

FIG. 5A depicts a diagram of tasks migration for one or more wirelessscheduling inputs, according to embodiments of the present disclosure.

FIG. 5B depicts a diagram of full migration for wireless schedulinginputs, according to embodiments of the present disclosure.

FIG. 6 depicts a process of wireless scheduling migration, according toembodiments of the present disclosure.

FIG. 7 depicts a process of local control of resource allocation,according to embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following description, for purposes of explanation, specificdetails are set forth in order to provide an understanding of thedisclosure. It will be apparent, however, to one skilled in the art thatthe disclosure can be practiced without these details. Furthermore, oneskilled in the art will recognize that embodiments of the presentdisclosure, described below, may be implemented in a variety of ways,such as a process, an apparatus, a system/device, or a method on atangible computer-readable medium.

Components, or modules, shown in diagrams are illustrative of exemplaryembodiments of the disclosure and are meant to avoid obscuring thedisclosure. It shall also be understood that throughout this discussionthat components may be described as separate functional units, which maycomprise sub-units, but those skilled in the art will recognize thatvarious components, or portions thereof, may be divided into separatecomponents or may be integrated together, including, for example, beingin a single system or component. It should be noted that functions oroperations discussed herein may be implemented as components. Componentsmay be implemented in software, hardware, or a combination thereof.

Furthermore, connections between components or systems within thefigures are not intended to be limited to direct connections. Rather,data between these components may be modified, re-formatted, orotherwise changed by intermediary components. Also, additional or fewerconnections may be used. It shall also be noted that the terms“coupled,” “connected,” “communicatively coupled,” “interfacing,”“interface,” or any of their derivatives shall be understood to includedirect connections, indirect connections through one or moreintermediary devices, and wireless connections. It shall also be notedthat any communication, such as a signal, response, reply,acknowledgement, message, query, etc., may comprise one or moreexchanges of information.

Reference in the specification to “one or more embodiments,” “preferredembodiment,” “an embodiment,” “embodiments,” or the like means that aparticular feature, structure, characteristic, or function described inconnection with the embodiment is included in at least one embodiment ofthe disclosure and may be in more than one embodiment. Also, theappearances of the above-noted phrases in various places in thespecification are not necessarily all referring to the same embodimentor embodiments.

The use of certain terms in various places in the specification is forillustration and should not be construed as limiting. The terms“include,” “including,” “comprise,” and “comprising” shall be understoodto be open terms and any examples are provided by way of illustrationand shall not be used to limit the scope of this disclosure.

A service, function, or resource is not limited to a single service,function, or resource; usage of these terms may refer to a grouping ofrelated services, functions, or resources, which may be distributed oraggregated. The use of memory, database, information base, data store,tables, hardware, cache, and the like may be used herein to refer tosystem component or components into which information may be entered orotherwise recorded. The terms “data,” “information,” along with similarterms, may be replaced by other terminologies referring to a group ofone or more bits, and may be used interchangeably. The terms “packet” or“frame” shall be understood to mean a group of one or more bits. Theterm “frame” or “packet” shall not be interpreted as limitingembodiments of the present invention to 5G networks. The terms “packet,”“frame,” “data,” or “data traffic” may be replaced by otherterminologies referring to a group of bits, such as “datagram” or“cell.” The words “optimal,” “optimize,” “optimization,” and the likerefer to an improvement of an outcome or a process and do not requirethat the specified outcome or process has achieved an “optimal” or peakstate.

It shall be noted that: (1) certain steps may optionally be performed;(2) steps may not be limited to the specific order set forth herein; (3)certain steps may be performed in different orders; and (4) certainsteps may be done concurrently.

A. Open RAN Deployment Models

A radio access network (RAN) is part of a telecommunication system. Itimplements a radio access technology (RAT) to provide connection betweena device, e.g., a mobile phone, and a core network (CN). Open RAN is anapproach based on interoperability and standardization of RAN elementsincluding a unified interconnection standard for white-box hardware andopen source software elements from different vendors.

FIG. 1 depicts various open radio access network (RAN) deployments for atelecommunication service provider, according to embodiments of thepresent disclosure. As shown in FIG. 1 , a radio unit (RU) 102 maycouple to a virtual distribution unit (vDU) 112 with a split, e.g., ORAN7-2 split, which is a Low PHY/High PHY split for ultra-reliablelow-latency communication (URLLC) and near-edge deployment. The vDU 112then couples to a virtual central unit (vCU) 122 with a split, e.g.,split 2, which is referred as radio resource control and packet dataconvergence control split from the Layer 2 radio link control (RLC).Alternatively, a vDU may be deployed on the side of an RU 104, and thencouples to a vCU 124 with a split, e.g., split 2. Alternatively, adistribution unit (DU) and an RU may be integrated as an appliance 106,which then couples to a vCU 126 with a split, e.g., split 2.Alternatively, a RU may be a small cell RN (S-RU) 108 couples to a smallcell DU or vDU (S-vDU) 118 using a split, e.g., a MAC/PHY layer split(split 6). The S-vDU 118 then couple to a vCU 128 with a split, e.g.,split 2.

A service provider (SP) may adopt more than one Open RAN deploymentmodels based on band, fronthaul bandwidth requirements, or deploymenttype (macro/small cell), etc. Deployment models are influenced ordecided based on multiple factors, including Fibre availability,real-estate/site/location constraints at pre-aggregation (Pre-Agg) andcell sites, total cost of ownership (TCO), Operational preference, etc.It is desirable for SPs to achieve maximum consistency aroundarchitecture, systems and operational model across all these deploymentmodels.

B. Resource Allocation across Multiple Wireless Scheduling Inputs

In a dis-aggregated open and virtualized RAN architecture, multiplecarriers may be converged in a DU which implements, among otheroperations, layer-2 scheduler and some higher operations on the physicallayer.

Operations of components on the physical layer may be criticallydifferent for various applications. For example, a scheduler may beimplemented to optimize retransmissions for data traffic, or to optimizesemi-persistent allocations for voice or low latency applications. Inanother example, some applications may involve higher density ofpilots/reference signals for fast moving users, e.g., drones. There aremany such optimizations being served within a coverage region. In one ormore embodiments of the present patent document, the different types ofapplications are considered as scheduling inputs as each of them use aslice of the resource (or at least resources on the physical layer andthe layer-2 scheduler) for a specific application type.

In certain situations, various carriers may be aggregated at a DU, whichresults in multiple slices aggregating for the physical layerimplementations. Resources required for each of the application is aslice of the overall resources, e.g., compute capability. Reservingexcessive resources for a slice of the spectrum or resource is not onlywasteful, but also lowers overall system operation efficiency andperformance. The present patent document discloses embodiments ofmanaging multiple slices actively and/or optimally across multipleapplications in single hardware architecture.

FIG. 2 depicts a central control unit for managing multiple wirelessscheduling inputs across various applications, according to embodimentsof the present disclosure. The center processing unit 210 receives aplurality of wireless scheduling inputs for aggregated physical layerimplementations. The center processing unit 210 comprises a plurality ofconfigurable processing units, which are allocated to process theplurality of wireless scheduling inputs based at least on a resourcecontrol signal 232 output from a resource controller 230. In one or moreembodiments, the center processing unit 210 and the resource controller230 are integrated within a DU for real time L1 and L2 schedulingfunctions.

The plurality of wireless scheduling inputs may be scheduling requestson a physical layer in a virtualized setting, e.g., vPHY0 202, vPHY1204, and vPHYN 205. The wireless scheduling inputs may be from the sameDU or different DUs. In one or more embodiments, each wirelessscheduling input may be defined as a type or a group that comprises oneor more scheduling requests that are of the same or similar applicationtype. Each scheduling input may have its own specific requirements orpreference for operation parameters, e.g., latency, throughput, etc. Forexample, the scheduling input vPHY0 202 may comprise one or morescheduling requests to support one or more autonomous driving vehicles,while scheduling input vPHY1 204 may comprise one or more schedulingrequests to service one or more IoT devices.

Upon receiving the plurality of wireless scheduling inputs, the centerprocessing unit 210 adjustably allocates the plurality of configurableprocessing units to process the plurality of wireless scheduling inputs.The allocation may be based on at least one of: current status of theplurality of configurable processing units, one or more priority rules,and a resource control signal 232 output from a resource controller 230.In one or more embodiments, the center processing unit 210 may create aplurality of computation instances with each computation instancehandling a wireless scheduling input. Each computation instance consumesa slice of overall hardware, software, or hardware and software resource(or at least resources on the physical layer) of the center processingunit 210. Each computation instance may be independently managed,controlled, and customized according to the specific requirement(s) ofthe corresponding wireless scheduling input. The one or more priorityrules for resource allocation may comprise an instance priority rule toset priorities among different slices or computation instances, and/or acarrier priority rule for multiple carriers within a slice. Implementingone or more priority rules is important and valuable to allow scaling ofvery large number of scheduling inputs.

In one or more embodiments, the center processing unit 210 is amulti-core processing unit with a plurality of cores serving asconfigurable processing units for designated tasks. The centerprocessing unit 210 may designate one or more cores 212 for taskscheduling and the core(s) for task scheduling may function as sharedresources among the plurality of computation instances. The centerprocessing unit 210 may also assign one or more cores 214 dedicated to acomputation instance for task execution. Shared resources may bedetermined every slot or subframe based on dedicated resources in thatslot or subframe for each computation instance. The creation of acomputation instance may involve at least one of: core assignment, loadappropriate binaries, local memory (LMEM) provisioning, etc. Acomputation instance may be designated to handle multiple carriers orone carrier with multiple bandwidth parts (BWPs), A computation instancemay be also specifically designated by tasks, e.g., acceleration forforward error correction (FEC), enhanced common public radio interface(eCRPI), or machine learning (ML).

The center processing unit 210 may comprise an LMEM 220 which stores oneor more task tables 222 for core assignment. The task tables may bepreloaded or dynamically established based at least on the plurality ofwireless scheduling inputs received at the center processing unit 210.In one or more embodiments, the LMEM 220 may comprise memory allocationsstoring codes for computation management, application programminginterface (API) data structure, and computation instance log records,etc.

During computation instance creation or execution, the center processingunit 210 may implement one or more operations comprising operations,administration and maintenance (OAM) adaptation, and/or MAC-PHY (orL2-L1) interface translation.

In one or more embodiments, a configurable processing unit in the centerprocessing unit 210 is a core based on various architecture, e.g., ARM,x86, RISC-V, etc. architecture. Cores in the center processing unit 210may be highly configurable for various tasks. One skilled in the artwill recognize that various types of processing cores may be implementedacross different embodiments of the invention.

Upon implementing the plurality of computation instances usingrespectively allocated resources, the center processing unit 210generates one or more commands (CMDs), which are transmitted to one ormore respectively hardware acceleration components for furtherprocessing. The one or more commands may comprise one or more commandsto one or more medium access control accelerators (MXL) 240 fortransport block (TB) processing, one or more enhanced common publicradio interface (eCPRI) commands to control one or more eCPRIs 250, oneor more forward error correction (FEC) commands to an encoder/decoderunit 260, one or more signal processing engine (SPE) commands to controla SPE 270. The SPE 270 may comprise a plurality of SPE units toimplement channel estimation, measurements, equalization, etc.

FIG. 3 depicts a process of allocating resources among multiple wirelessscheduling inputs, according to embodiments of the present disclosure.In step 305, a central control unit, which resides within a DU, receivesa plurality of wireless scheduling inputs across various applications,e.g., autonomous driving, IoT, etc. The central control unit comprises aplurality of configurable processing units which may be dynamicallyallocated for processing various tasks. In step 310, the central controlunit creates multiple computation instances with each computationinstance corresponding to one scheduling input. Each computationinstance is allocated with a slice of resources in the central controlunit. The slice assigned to an instance may comprise dedicated resourcesspecific to the slice and shared resources among the multiple slices.Resource slicing at the central control unit may be based on at leastone of current status of the plurality of configurable processing units,one or more priority rules, and a resource control signal output from aresource controller. In step 315, multiple computation instances areimplemented to generate one or more commands at a desired level ofgranularity to control one or more hardware acceleration components. Instep 320, the one or more hardware acceleration components respectivelyimplement the one or more commands for desired operations.

C. Embodiments of PHY Policy Implementation

Upon receiving a plurality of scheduling inputs, the central controlunit creates multiple computation instances by slicing resources intomultiple slices to process the scheduling inputs. Resources to be slicedmay comprise hardware, software, memory, or a combination thereof, andmay be driven from core down to RAN. Resource slicing may be dynamicallyconfigured based on one or more priorities and policies. In one or moreembodiments, resource slicing may involve resource allocation amongdifferent scheduling inputs, and also among various carries within thesame scheduling input. For example, one of the multiple schedulinginputs may be related to automatous driving service for multipleautomatous vehicles. The central control unit needs to be implementresource allocation not only between automatous driving services andother types of scheduling inputs, but also among the multiple automatousvehicles.

In certain situations, a central control unit, such as a singlelarge-core host, may receive excessive scheduling inputs which arechallenging to be served for PHY implementation in a constrained timebound. Accordingly, an efficient and dynamic PHY policy implementationmay be necessary. FIG. 4 depicts a schematic diagram for PHY policyimplementation, according to embodiments of the present disclosure. Asshown in FIG. 4 , the central control unit 410 is a single large-corehost comprising multiple configurable processing units 414, 416, and418, etc., which may be single cores configurable for implementingvarious scheduling tasks. The central control unit 410 may also comprisea decision module 412 coupled to control the multiple configurableprocessing units for desired slice or computation instance allocation.The decision module 412 may be a core (e.g., the core 212 shown in FIG.2 ) specifically designated for task scheduling among other cores withinthe central control unit 410. The decision module 412 receives decisionmanagement data 426 resulting from a MAC-PHY interface translationoperation 422, which may at least involve codes or firmware loadedwithin a memory 423 allocated for computation management, API datastructure information within a memory 424 allocated for API datastructure storage, and a resource scheduling control signal output froma resource scheduler 425. The slot message 411 to be processed in theMAC-PHY interface translation operation may comprise slot/PHY tasksmessages related to multiple scheduling inputs (e.g., vPHY0, vPHY1,vPHYN shown in FIG. 2 ) and may also comprise extensions for priorityamong the PHY tasks.

In one or more embodiments, the memory 423 allocated for computationmanagement is loaded with management information of the physical layer430, such that situation of the physical layer 430 may be taken intoconsideration during the MAC-PHY interface translation operation 422.The management information may comprise resource management data, andchannel management data, etc.

D. Embodiments of PHY Tasks Migration

In certain situations, a host may receive excessive scheduling inputs.As a result, the configurable processing units in the host may beover-provisioned. When this happens, the host or the resource schedulermay implement dynamic resource allocation such that the configurableprocessing units are not synchronized for increased PHY schedulingcapacity to meet the needs. If such an asynchronous operation is notadequate or a specific criteria is met, e.g., one or more quality ofservice (QoS) parameters (latency, throughput, error rate, etc.)reaching a threshold, the host or the resource scheduler may need tomigrate one or more currently served scheduling inputs to anotherdistribution unit (DU), preferably without causing service interruptionto end-users.

FIG. 5A depicts a diagram of tasks migration for one or more wirelessscheduling inputs, according to embodiments of the present disclosure.As shown in FIG. 5A, a first host (or a first central control unit) 516and a first resource scheduler and PHY 518 both reside within a first DU512, which coupes to a first control unit (CU) 510. The first DU 512couples to a first radio unit (RU) 514 via a first fronthaul link 515. Asecond host (or a second central control unit) 526 and a second resourcescheduler and PHY 528 both reside within a second DU 522, which coupesto a second CU 520. The second DU 522 couples to a second RU 524 via asecond fronthaul link 525.

PHY scheduling migration may be initiated by the first host 516, thefirst resource scheduler 518, or the first CU 510. When schedulinginputs from the first RU 514 are excessive (e.g., at least one QoSparameter reaching a threshold, or one of the slices of resourcesreaching a minimum resource threshold), the first host 516 sends a slotmessage 517 comprising extensions of one or more PHY tasks for migrationto the first resource scheduler and PHY 518. The selection of the PHYtasks for migration may be based on one or more instance priority rulesto set priorities among different slices or computation instances,and/or one or more carrier priority rules for multiple carriers within aslice. The first resource scheduler and PHY 518 communicates to thefirst RU 515 such that the first RU 515 transmits scheduling inputsassociated with the one or more PHY tasks for migration to the secondresource scheduler and PHY 528 via a third fronthaul link 535, besidesto the first resource scheduler and PHY 518 via a first fronthaul link515. The second host 526 sends a carrier message 527 comprisingextensions to add the one or more PHY tasks for resource allocation tothe second resource scheduler and PHY 528. Once the second resourcescheduler and PHY 528 allocates resources for the one or more PHY tasks,the first RU 514 stops transmitting scheduling request for the one ormore PHY tasks to the first DU 512. Such a make-before-break approachensures no service interruption to end users. In certain situations, thefirst DU 512 may even have power down and migrate all PHY tasks to thesecond DU 522, as shown in FIG. 5B.

FIG. 6 depicts a process of wireless scheduling migration, according toembodiments of the present disclosure. In step 605, responsive tospecific criteria being met when multiple compute instances areimplemented in a first central control unit at a first DU correspondingto one or more scheduling inputs from a first RU, the first centralcontrol unit selects at least one scheduling input for migrationaccording to one or more rules. The one or more rules may comprise onone or more instance priority rules to set priorities among differentslices or computation instances, and/or one or more carrier priorityrules for multiple carriers within a slice. In step 610, a fronthaullink between a first radio unit and a second DU is established for thefirst RU to transmit the at least one scheduling input to the second DUbesides the first DU. In step 615, a second resource scheduler withinthe second DU implements resource allocation in a second host for the atleast one scheduling input. In step 625, the first resource schedulerupdates resource allocation for other scheduling inputs transmitted fromthe first RU.

Embodiments of PHY tasks migration may enable seamlessly adding orremoving a computation instance or a slice of resources. Additionally,prioritization may be implemented for existing slices when overloadinghappens, such that slices with higher priority may be allocated withmore resources for more aggressive implementation. The make-before-breakmigration approach as shown in FIG. 5A allows non interruption for endusers during scheduling migration, thus increases robustness for thecommunication service.

FIG. 7 depicts a process of local control of resource allocation,according to embodiments of the present disclosure. In step 705, anacceleration component receives one or more commands transmitted fromthe central control unit. The acceleration component may be a hardwareacceleration component, a software acceleration component, or acombination thereof. For example, the acceleration component may be anencoder/decoder unit, a signal processing engine, an eCPRI, etc. In step710, a local control firmware schedules the hardware accelerationcomponent for desired operation according to information in the one ormore commands for desired or improved throughput.

Aspects of the present disclosure may be encoded upon one or morenon-transitory computer-readable media with instructions for one or moreprocessors or processing units to cause steps to be performed. It shallbe noted that the one or more non-transitory computer-readable mediashall include volatile and/or non-volatile memory. It shall be notedthat alternative implementations are possible, including a hardwareimplementation or a software/hardware implementation.Hardware-implemented functions may be realized using ASIC(s),programmable arrays, digital signal processing circuitry, or the like.Accordingly, the “means” terms in any claims are intended to cover bothsoftware and hardware implementations. Similarly, the term“computer-readable medium or media” as used herein includes softwareand/or hardware having a program of instructions embodied thereon, or acombination thereof. With these implementation alternatives in mind, itis to be understood that the figures and accompanying descriptionprovide the functional information one skilled in the art would requireto write program code (i.e., software) and/or to fabricate circuits(i.e., hardware) to perform the processing required.

It shall be noted that embodiments of the present disclosure may furtherrelate to computer products with a non-transitory, tangiblecomputer-readable medium that have computer code thereon for performingvarious computer-implemented operations. The media and computer code maybe those specially designed and constructed for the purposes of thepresent disclosure, or they may be of the kind known or available tothose having skill in the relevant arts. Examples of tangiblecomputer-readable media include, for example: magnetic media such ashard disks, floppy disks, and magnetic tape; optical media such asCD-ROMs and holographic devices; magneto-optical media; and hardwaredevices that are specially configured to store or to store and executeprogram code, such as application specific integrated circuits (ASICs),programmable logic devices (PLDs), flash memory devices, othernon-volatile memory (NVM) devices (such as 3D XPoint-based devices), andROM and RAM devices. Examples of computer code include machine code,such as produced by a compiler, and files containing higher level codethat are executed by a computer using an interpreter. Embodiments of thepresent disclosure may be implemented in whole or in part asmachine-executable instructions that may be in program modules that areexecuted by a processing device. Examples of program modules includelibraries, programs, routines, objects, components, and data structures.In distributed computing environments, program modules may be physicallylocated in settings that are local, remote, or both.

One skilled in the art will recognize no computing system or programminglanguage is critical to the practice of the present disclosure. Oneskilled in the art will also recognize that a number of the elementsdescribed above may be physically and/or functionally separated intomodules and/or sub-modules or combined together.

It will be appreciated to those skilled in the art that the precedingexamples and embodiments are exemplary and not limiting to the scope ofthe present disclosure. It is intended that all permutations,enhancements, equivalents, combinations, and improvements thereto thatare apparent to those skilled in the art upon a reading of thespecification and a study of the drawings are included within the truespirit and scope of the present disclosure. It shall also be noted thatelements of any claims may be arranged differently including havingmultiple dependencies, configurations, and combinations.

What is claimed is:
 1. A method for resource allocation across wirelesscommunication applications comprising: creating, at a wirelesscommunication device comprising configurable processing resources,multiple computation instances with each computation instancecorresponding to one of multiple scheduling tasks for multiple wirelesscommunication applications, each computation instance is allocated witha slice of the configurable processing resources; and implementing themultiple computation instances to generate one or more commands for eachof the multiple scheduling tasks, the one or more commands comprising atleast one of: one or more transport block (TB) processing commands; oneor more enhanced common public radio interface (eCPRI) commands; one ormore forward error correction (FEC) commands; and one or more signalprocessing engine (SPE) commands.
 2. The method of claim 1, wherein eachslice of resources comprises dedicated resources specific to each slice.3. The method of claim 2, wherein each slice of resources furthercomprises shared resources among at least two slices of the configurableprocessing resources.
 4. The method of claim 3, wherein the sharedresources are determined based on dedicated resources for each of themultiple computation instances.
 5. The method of claim 1, wherein eachcomputation instance is designated to handle multiple carriers or onecarrier with multiple bandwidth parts (BWPs).
 6. The method of claim 1,wherein each computation instance is designated to handle one ofmultiple computation tasks which comprise one of more of: accelerationfor forward error correction (FEC); eCRPI communication; and machinelearning (ML).
 7. The method of claim 1, wherein the wirelesscommunication device is an access point, a distribution unit, or awireless base station.
 8. The method of claim 1, wherein each schedulingtask corresponds to one wireless communication application or one typeof wireless communication applications among the multiple wirelesscommunication applications.
 9. The method of claim 1 further comprising:transmitting the one or more commands to one or more accelerationcomponents for implementation, the one or more acceleration componentscomprise one or more of: one or more encoders; one or more decoders; oneor more signal processing engines (SPEs); and one or more enhancedcommon public radio interfaces (eCRPIs).
 10. The method of claim 1,wherein information of allocating the configurable processing resourcesamong the multiple computation instances is stored in one or more tasktables in a local memory (LMEM), the one or more task tables arepreloaded or dynamically established.
 11. A wireless communicationdevice comprising: a plurality of processing resources that areconfigurable to create multiple computation instances with eachcomputation instance corresponding to one of multiple scheduling tasksfor multiple wireless communication applications, each computationinstance is allocated with a slice of the plurality of processingresources; and wherein each slice of resources comprises dedicatedresources specific to each slice and shared resources among at least twoslices of the configurable processing resources, the shared resourcesare determined based on dedicated resources for each of the multiplecomputation instances.
 12. The wireless communication device of claim11, wherein the shared resources are determined every slot or subframebased on dedicated resources in that slot or subframe for eachcomputation instance.
 13. The wireless communication device of claim 11,wherein each computation instance is designated to handle multiplecarriers or one carrier with multiple bandwidth parts (BWPs).
 14. Thewireless communication device of claim 11, wherein each computationinstance is independently managed, controlled, or customized accordingto one or more specific requirements of the corresponding schedulingtask.
 15. The wireless communication device of claim 11, wherein eachscheduling task corresponds to one wireless communication application orone type of wireless communication applications among the multiplewireless communication applications, the multiple wireless communicationapplications comprise one or more of: one or more applications forUltra-Reliable Low-Latency Communication (URLLC); one or moreapplications for drones; one or more applications for enhanced MobileBroadband (eMBB); one or more applications for autonomous driving; andone or more applications for Internet of things (IoT).
 16. The wirelesscommunication device of claim 11, wherein the multiple computationinstances are implemented to generate one or more commands for each ofthe multiple scheduling tasks, the one or more commands comprising atleast one of: one or more transport block (TB) processing commands; oneor more enhanced common public radio interface (eCPRI) commands; one ormore forward error correction (FEC) commands; and one or more signalprocessing engine (SPE) commands.
 17. The wireless communication deviceof claim 11 further comprises: a resource controller generating aresource control signal, the multiple computation instances are createdbased at least on the resource control signal.
 18. The wirelesscommunication device of claim 17, wherein the resource scheduler and thecentral control unit are integrated for real time layer 1 (L1) and layer2 (L2) scheduling functions.
 19. The wireless communication device ofclaim 11 further comprising: one or more acceleration components coupledto receive the one or more commands for implementation.
 20. The wirelesscommunication device of claim 19, wherein one or more accelerationcomponents comprise one or more of: one or more encoders; one or moredecoders; one or more signal processing engines (SPEs); and one or moreenhanced common public radio interfaces (eCRPIs).